Quantitative assessment of myocardial fibrosis in an age-related rat model by ex vivo late gadolinium enhancement magnetic resonance imaging with histopathological correlation

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Zinc's protective role against hydroxychloroquine-induced cardiac effects in adult male albino rats

Background

Long exposure to Hydroxychloroquine (HCQ) has been complicated by some dangerous though infrequent cardiotoxicity.

Methods

A total of 40 normal adult male albino rats dispersed into 4 groups were used. Group 1 (Control group), Group II (HCQ treated group), Group III (zinc [Zn]-treated group), and Group IV (HCQ and Zn treated group). Once the experimentation ended, rats were sacrificed and cardiac soft tissue sections were processed twenty-four hours at the end of the experiment for histological study.

Results

Cardiac-stained sections revealed that HCQ induced widespread necrosis, dilatation, and vacuolar degeneration. However, the combination of HCQ with Zn ameliorated these damaging effects. Cardiac enzyme parameters were also studied in the 4 groups and revealed CK-MB and troponin were considerably elevated in groups II associated to the control group.

Conclusion

It was concluded that Zn revealed a protective role against HCQ cardiomyopathy in adult male albino rats. This might signify an appreciated means for Zn-based treatment in the upcoming subsequent clinical records to adjust doses and guarantee patient safeguard.

Multiparametric cardiovascular magnetic resonance characteristics and dynamic changes in asymptomatic heart-transplanted patients

OBJECTIVES

To describe the dynamic changes in cardiac deformation and tissue characteristics using cardiac magnetic resonance (CMR) in asymptomatic patients during 12 months after heart transplantation (HT).

METHODS

From April 2020 to January 2021, 21 consecutive HT patients without clinical symptoms were included in this prospective study. Multiparametric CMR was performed at 3, 6, and 12 months after HT. Twenty-five healthy volunteers served as controls.

RESULTS

During follow-up, a decline in left ventricular (LV) global radial strain (GRS) (p = 0.020) and right ventricular (RV) global longitudinal strain (GLS) (p < 0.001) and an increase in post-contrast T1 (p = 0.024) and T2 (p < 0.001) in asymptomatic HT patients occurred at 3 months, which normalized at 6 months postoperatively, compared with those in healthy controls. A decline in LVGLS (p < 0.001) and LV global circumferential strain (GCS) (p < 0.001) and an increase in native T1 (p < 0.001), T2 (p < 0.001), and extracellular volume (ECV) (p < 0.001) occurred at 3 months. Although most parameters improved gradually, LVGLS, native T1, and ECV remained abnormal compared with those in healthy controls at 12 months; only T2 and LVGCS were normalized at 6 months and 12 months, respectively. ECV was significantly correlated with LVGLS, LVGCS, and LVGRS.

CONCLUSION

Cardiac deformation and tissue characteristics were abnormal early after HT, although the patients were clinically asymptomatic. The dynamic changes in CMR characteristics demonstrate a gradual recovery of myocardial injury associated with transplantation during the first 12 months after HT.

KEY POINTS

• Multiparametric CMR can detect the dynamic changes of transplantation-associated myocardial injury. • Post-contrast T1, T2, LVGRS, and RVGLS values are normalized at 6 months after HT. • Native T1, ECV, and LVGLS values remain abnormal compared with those in healthy controls at 12 months after HT.

Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.

Determination of scar area using native and post-contrast T1 mapping: Agreement with late gadolinium enhancement

The purpose of this study is to ascertain agreement in measurements of the scar area between late gadolinium enhancement (LGE), native and post-contrast T1 mapping in patients with known ischemic heart disease. 132 patients (age 60 ± 11 yrs, male 82%) were included in the study. Corresponding 3 short axis slices images of LGE, native and post contrast T1 mapping were used. Scar area was evaluated semi- quantitatively with FWHM methods, in which scar is automatically determined by specialized post-processing software. Agreement per culprit vessel was also assessed. Concordance and inter- intraobserver reproducibility were assessed with Bland-Altman analysis. The results show that scar area amounted to 12.6% of myocardium for LGE, 9.1% for native (p < 0.05) and 19.4% (p < 0.05) for post-contrast T1 mapping. LAD and RCA territory infarcts showed statistical discrepancy for both T1 acquisitions. Intraobserver differences in infarct size were comparable at 0.39% ± 0.28, 2.93% ± 0.03 and 0.97% ± 0.01 respectively (p≫0.05). Interobserver differences were 5.56% ± 0.91 for LGE, 11.87% ± 3.21 (p < 0.05) for native and 5.55% ± 2.87 (p≫0.05) for post-contrast T1 mapping. In conclusion, native T1 acquisitions systematically underestimated infarct size in comparison to LGE, while post-contrast T1 overestimated it. Variances in measurements were most pronounced for LAD and RCA territory infarcts. Intraobserver reproducibility was similar with both methods, whereas interobserver variability for native T1 mapping acquisition was worse.

Artificial intelligence and cardiovascular imaging: A win-win combination

Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.

Apparent growth tensor of left ventricular post myocardial infarction - In human first natural history study

An outstanding challenge in modelling biomechanics after myocardial infarction (MI) is to estimate the so-called growth tensor. Since it is impossible to track pure growth induced geometry change from in vivo magnetic resonance images alone, in this work, we propose a way of estimating a surrogate or apparent growth tensor of the human left ventricle using cine magnetic resonance (CMR) and late gadolinium enhanced (LGE) images of 16 patients following acute MI. The apparent growth tensor is evaluated at four time-points following myocardial reperfusion: 4-12 h (baseline), 3 days, 10 days and 7 months. We have identified three different growth patterns classified as the Dilation, No-Change and Shrinkage groups defined by the left ventricle end-diastole cavity volume change from baseline. We study the- trends in both the infarct and remote regions. Importantly, although the No-Change group has little change in the ventricular cavity volume, significant remodelling changes are seen within the myocardial wall, both in the infarct and remote regions. Through statistical analysis, we show that the growth tensor invariants can be used as effective biomarkers for adverse and favourable remodelling of the heart from 10 days onwards post-MI with statistically significant changes over time, in contrast to most of the routine clinical indices. We believe this is the first time that the apparent growth tensor has been estimated from in vivo CMR images post-MI. Our study not only provides much-needed information for understanding growth and remodelling in the human heart following acute MI, but also identifies novel biomarker for assessing heart disease progression.

Quantification of late gadolinium enhancement cardiovascular MRI in patients with coronary artery chronic total occlusion

AIM

To determine the most accurate and reproducible semi-automated greyscale thresholding technique for quantifying late gadolinium enhancement (LGE) in cardiovascular magnetic resonance imaging (CMRI), by using positron-emission tomography (PET) as the reference standard in patients with coronary artery chronic total occlusion (CTO).

MATERIALS AND METHODS

LGE in CMRI, single-photon-emission computed tomography (SPECT), and PET were performed within 1 week in each of 63 patients with known CTO. The presence and quantity of LGE were determined with greyscale thresholds of 2, 4, 5, 6, and 8 standard deviations (SDs) above the mean signal intensity for normal remote myocardium and full width at half maximum (FWHM). The infarcted myocardium was delineated by PET.

RESULTS

Sixty-three patients and 1,008 segments were analysed. Based on patient analysis, with PET as the reference standard, the 5 SD method yielded the strongest correlation (r=0.85, p<0.0001) compared with the 2 SDs (r=0.42), 4 SDs (r=0.73), 6 SDs (r=0.81), 8 SDs (r=0.71), and FWHM (r=0.69; p<0.001 for all comparisons). The 5 SDs threshold quantification showed high interobserver and intra-observer agreement (intraclass correlation coefficient [ICC]=0.90, p<0.0001; ICC=0.93, p<0.0001, respectively).

CONCLUSIONS

Semi-automated LGE CMRI greyscale thresholding with 5 SDs above the mean signal intensity for normal remote myocardium yields the strongest correlation to the extent of LGE identified using PET and is highly reproducible in patients with CTO.

Cardiovascular magnetic resonance-derived myocardial strain in asymptomatic heart transplanted patients and its correlation with late gadolinium enhancement

OBJECTIVES

To investigate whether cardiovascular magnetic resonance (CMR)-derived myocardial strains were abnormal in asymptomatic heart transplant (HT) patients with normal left ventricular ejection fraction (LVEF) and to detect the relationship between CMR-derived myocardial strain parameters and late gadolinium enhancement (LGE) in asymptomatic HT patients.

METHODS

A total of 72 HT patients and 35 healthy volunteers underwent 1.5-T MR scanning. The examination protocol included basic cine imaging and LGE. The deformation registration algorithm (DRA) and feature tracking (FT) software were used for the strain analyses. Myocardial strain measurements included left ventricular global longitudinal strain (LVGLS), LV global circumferential strain (LVGCS), LV global radial strain (LVGRS) and right ventricular longitudinal strain (RVLS).

RESULTS

Compared with healthy volunteers, HT patients had significantly decreased DRA- and FT- derived myocardial strain measurements (all p < 0.05). There was a significant correlation and high reproducibility between the DRA- and FT-derived strain parameters. Both CMR-derived LVGLS and LVGRS were significantly related to the presence of LGE, and multivariate logistic regression analyses showed that the LVGLS measurement obtained from both techniques was independently associated with the presence of LGE. The odds ratios (ORs) for DRA- and FT-LVGLS were 1.340 and 1.342, respectively.

CONCLUSIONS

Asymptomatic HT patients with preserved LVEF exhibited reduced myocardial strain parameters. The CMR-derived LVGLS was independently related to the presence of LGE in HT patients.

KEY POINTS

• Reduced myocardial strain parameters were found in asymptomatic heart transplanted (HT) patients with normal left ventricular ejection fraction (LVEF). • The deformation registration algorithm (DRA) and feature tracking (FT)-derived strains in asymptomatic HT patients had high reproducibility. • DRA- and FT-derived LVGLS had an independent relationship with late gadolinium enhancement (LGE) in asymptomatic HT patients.

Machine learning in cardiovascular magnetic resonance: basic concepts and applications

Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.

Heart Histopathology and Mitochondrial Ultrastructure in Aged Rats Fed for 24 Months on Different Unsaturated Fats (Virgin Olive Oil, Sunflower Oil or Fish Oil) and Affected by Different Longevity

Diet plays a decisive role in heart physiology, with lipids having especial importance in pathology prevention and development. This study aimed to investigate how dietary lipids varying in lipid profile (virgin olive oil, sunflower oil or fish oil) affected the heart of rats during aging. Heart histopathology, mitochondrial morphometry, and oxidative status were assessed. Typical histopathological features associated with aging, such as valvular lesions, endomyocardical hyperplasia, or papillary muscle calcification, were found at a low extent in all the experimental groups. The most relevant finding was that inflammation registered by fish oil group was lower compared to the other treatments. At the ultrastructural level, heart mitochondrial area, perimeter, and aspect ratio were higher in fish oil-fed rats than in those fed on sunflower oil. Concerning oxidative stress markers, there were differences only in coenzyme Q levels and catalase activity, lower in sunflower oil-fed animals compared with those fed on fish oil. In summary, dietary intake for a long period on dietary fats with different fatty acids profile led to differences in some aspects associated with the aging process at the heart. Fish oil seems to be the fat most protective of heart during aging.

Assessment of Remote Myocardium Heterogeneity in Patients with Ventricular Tachycardia Using Texture Analysis of Late Iodine Enhancement (LIE) Cardiac Computed Tomography (cCT) Images

Purpose

Diffuse remodeling of myocardial extra-cellular matrix is largely responsible for left ventricle (LV) dysfunction and arrhythmias. Our hypothesis is that the texture analysis of late iodine enhancement (LIE) cardiac computed tomography (cCT) images may improve characterization of the diffuse extra-cellular matrix changes. Our aim was to extract volumetric extracellular volume (ECV) and LIE texture features of non-scarred (remote) myocardium from cCT of patients with recurrent ventricular tachycardia (rVT), and to compare these radiomic features with LV-function, LV-remodeling, and underlying cardiac disease.

Procedures

Forty-eight patients suffering from rVT were prospectively enrolled: 5/48 with idiopathic VT (IVT), 23/48 with post-ischemic dilated cardiomyopathy (ICM), 9/48 with idiopathic dilated cardiomyopathy (IDCM), and 11/48 with scars from a previous healed myocarditis (MYO). All patients underwent echocardiography to assess LV systolic and diastolic function and cCT with pre-contrast, angiographic, and LIE scan to obtain end-diastolic volume (EDV), ECV, and first-order texture parameters of Hounsfield Unit (HU) of remote myocardium in LIE [energy, entropy, HU-mean, HU-median, standard deviation (SD), and mean absolute deviation (MAD)].

Results

Energy, HU mean, and HU median by cCT texture analysis correlated with ECV (rho = 0.5650, rho = 0.5741, rho = 0.5068; p < 0.0005). cCT-derived ECV, HU-mean, HU-median, SD, and MAD correlated directly to EDV by cCT and inversely to ejection fraction by echocardiography (p < 0.05). SD and MAD correlated with diastolic function by echocardiography (rho = 0.3837, p = 0.0071; rho = 0.3330, p = 0.0208). MYO and IVT patients were characterized by significantly lower values of SD and MAD when compared with ICM and IDCM patients, independently of LV-volume systolic and diastolic function.

Conclusions

Texture analysis of LIE may expand cCT capability of myocardial characterization. Myocardial heterogeneity (SD and MAD) was associated with LV dilatation, systolic and diastolic function, and is able to potentially identify the different patterns of structural remodeling characterizing patients with rVT of different etiology.

Relationship between Extension or Texture Features of Late Gadolinium Enhancement and Ventricular Tachyarrhythmias in Hypertrophic Cardiomyopathy

Purpose

To evaluate the relationship between extension or texture features of late gadolinium enhancement (LGE) and ventricular tachyarrhythmias in hypertrophic cardiomyopathy (HCM).

Materials and Methods

Twenty-three patients with HCM were enrolled in this IRB-approved study. The extension of LGE was determined based on the American Heart Association segments model. Texture analysis was performed for 43 myocardial LGE using an open-access software (MaZda, Technical University of Lodz, Institute of Electronics, Poland). The relationship between the extension or texture features of LGE and ventricular tachyarrhythmias was evaluated using unpaired test and receiver-operating characteristic (ROC) analysis.

Results

Six of 23 patients had a history of ventricular tachyarrhythmias, and 16 patients had LGE. All of the 6 patients with the arrhythmias had more than 4 LGE segments and more LGE segments than those without (p < 0.01). Among 4 texture features, entropy LL was the only discriminator between the 2 patient groups (p < 0.01; threshold, 19624; area under the curve [AUC], 0.72). An ROC analysis gave the number of segments showing LGE a better result (AUC, 0.96) for identification of HCM patients with ventricular tachyarrhythmias than the entropy LL of LGE.

Conclusion

Patients with HCM and a history of ventricular tachyarrhythmias had a wider extension of LGE, and their entropy LL of LGE was significantly lower than those without. The extension of LGE and texture analysis may provide information about LGE related to ventricular tachyarrhythmias in HCM.

Association between myocardial extracellular volume and strain analysis through cardiovascular magnetic resonance with histological myocardial fibrosis in patients awaiting heart transplantation

BACKGROUND

Cardiovascular magnetic resonance (CMR)-derived extracellular volume (ECV) and tissue tracking strain analyses are proposed as non-invasive methods for quantifying myocardial fibrosis and deformation. This study sought (1) to histologically validate myocardial ECV against the collagen volume fraction (CVF) measured from tissue samples of patients undergoing heart transplantation and (2) to detect the correlations between myocardial systolic strain and the myocardial ECV and histological CVF in patients undergoing heart transplantation.

METHODS

A total of 12 dilated cardiomyopathy (DCM) and 10 ischaemic cardiomyopathy (ICM) patients underwent T1 mapping with the Modified Look Locker Inversion recovery (MOLLI) sequence, T2 mapping and ECV. Myocardial systolic strain, including left ventricular global longitudinal (GLS), circumferential (GCS) and radial strain (GRS), were quantified using CMR cine images with tissue tracking analysis software. Tissue samples were collected from each of 16 segments of the explanted hearts and were stained with picrosirius red for histological CVF quantification.

RESULTS

A strong relationship was observed between the global myocardial ECV and histological CVF in the DCM and ICM patients based on a per-patient analysis (r = 0.904 and r = 0.901, respectively, p <  0.001). In the linear mixed-effects regression analysis, ECV correlated well with the histological CVF in the DCM and ICM patients on a per-segment basis (β = 0.838 and β = 0.915, respectively, p <  0.001). In the multivariate linear regression analysis, histological CVF was the strongest independent determinant of ECV in the patients awaiting heart transplantation (standardised β = 0.860, p <  0.001). However, the T2 time, GLS, GCS and GRS showed no significant associations with ECV and CVF in the patients awaiting heart transplantation.

CONCLUSIONS

ECV derived from CMR correlated well with histological CVF, indicating its potential as a non-invasive tool for the quantification of myocardial fibrosis. Additionally, impaired myocardial systolic strains were not associated with the ECV and CVF in the patients awaiting heart transplantation.